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26 pages, 1809 KiB  
Review
Brain Stimulation Techniques in Research and Clinical Practice: A Comprehensive Review of Applications and Therapeutic Potential in Parkinson’s Disease
by Ata Jahangir Moshayedi, Tahmineh Mokhtari and Mehran Emadi Andani
Brain Sci. 2025, 15(1), 20; https://doi.org/10.3390/brainsci15010020 - 27 Dec 2024
Viewed by 373
Abstract
Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by a range of motor and non-motor symptoms (NMSs) that significantly impact patients’ quality of life. This review aims to synthesize the current literature on the application of brain stimulation techniques, including non-invasive methods [...] Read more.
Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by a range of motor and non-motor symptoms (NMSs) that significantly impact patients’ quality of life. This review aims to synthesize the current literature on the application of brain stimulation techniques, including non-invasive methods such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), transcranial focused ultrasound stimulation (tFUS), and transcutaneous vagus nerve stimulation (tVNS), as well as invasive approaches like deep brain stimulation (DBS). We explore the efficacy and safety profiles of these techniques in alleviating both motor impairments, such as bradykinesia and rigidity, and non-motor symptoms, including cognitive decline, depression, and impulse control disorders. Current findings indicate that while non-invasive techniques present a favorable safety profile and are effective for milder symptoms, invasive methods like DBS provide significant relief for severe cases that are unresponsive to other treatments. Future research is needed to optimize stimulation parameters, establish robust clinical protocols, and expand the application of these technologies across various stages of PD. This review underscores the potential of brain stimulation as a vital therapeutic tool in managing PD, paving the way for enhanced treatment strategies and improved patient outcomes. Full article
(This article belongs to the Special Issue Noninvasive Neuromodulation Applications in Research and Clinics)
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<p>The reviewed paper analyses: (<b>A</b>) review Prisma diagram, and (<b>B</b>) the papers based on publishers.</p>
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<p>Timeline of brain stimulation methods for the treatment of PD.</p>
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<p>Brain regions relevant to Parkinson’s Disease (PD). The areas highlighted include Substantia Nigra (01), Dopamine Pathway (02), Putamen (Striatum; 03), and Caudate Nucleus (Striatum; 04). The figure illustrates the five stages of PD, along with brain stimulation methods employed for PD patients.</p>
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26 pages, 1026 KiB  
Review
Efficacy of Transcranial Direct Current Stimulation (tDCS) on Neuropsychiatric Symptoms in Multiple Sclerosis (MS)—A Review and Insight into Possible Mechanisms of Action
by James Chmiel and Marta Stępień-Słodkowska
J. Clin. Med. 2024, 13(24), 7793; https://doi.org/10.3390/jcm13247793 - 20 Dec 2024
Viewed by 534
Abstract
Introduction: Neuropsychiatric symptoms such as depression and anxiety are a significant burden on patients with multiple sclerosis (MS). Their pathophysiology is complex and yet to be fully understood. There is an urgent need for non-invasive treatments that directly target the brain and [...] Read more.
Introduction: Neuropsychiatric symptoms such as depression and anxiety are a significant burden on patients with multiple sclerosis (MS). Their pathophysiology is complex and yet to be fully understood. There is an urgent need for non-invasive treatments that directly target the brain and help patients with MS. One such possible treatment is transcranial direct current stimulation (tDCS), a popular and effective non-invasive brain stimulation technique. Methods: This mechanistic review explores the efficacy of tDCS in treating depression and anxiety in MS while focusing on the underlying mechanisms of action. Understanding these mechanisms is crucial, as neuropsychiatric symptoms in MS arise from complex neuroinflammatory and neurodegenerative processes. This review offers insights that may direct more focused and efficient therapeutic approaches by investigating the ways in which tDCS affects inflammation, brain plasticity, and neural connections. Searches were conducted using the PubMed/Medline, ResearchGate, Cochrane, and Google Scholar databases. Results: The literature search yielded 11 studies to be included in this review, with a total of 175 patients participating in the included studies. In most studies, tDCS did not significantly reduce depression or anxiety scores as the studied patients did not have elevated scores indicating depression and anxiety. In the few studies where the patients had scores indicating mild/moderate dysfunction, tDCS was more effective. The risk of bias in the included studies was assessed as moderate. Despite the null or near-null results, tDCS may still prove to be an effective treatment option for depression and anxiety in MS, because tDCS produces a neurobiological effect on the brain and nervous system. To facilitate further work, several possible mechanisms of action of tDCS have been reported, such as the modulation of the frontal–midline theta, reductions in neuroinflammation, the modulation of the HPA axis, and cerebral blood flow regulation. Conclusions: Although tDCS did not overall demonstrate positive effects in reducing depression and anxiety in the studied MS patients, the role of tDCS in this area should not be underestimated. Evidence from other studies indicates the effectiveness of tDCS in reducing depression and anxiety, but the studies included in this review did not include patients with sufficient depression or anxiety. Future studies are needed to confirm the effectiveness of tDCS in neuropsychiatric dysfunctions in MS. Full article
(This article belongs to the Special Issue Multiple Sclerosis: Diagnosis, Treatment and Clinical Management)
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<p>Flowchart depicting the different phases of the systematic review.</p>
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<p>Flowchart describing potential mechanisms of action of tDCS in depressive and anxiety symptoms in MS.</p>
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15 pages, 915 KiB  
Review
Neurophysiologic Innovations in ALS: Enhancing Diagnosis, Monitoring, and Treatment Evaluation
by Ryan Donaghy and Erik P. Pioro
Brain Sci. 2024, 14(12), 1251; https://doi.org/10.3390/brainsci14121251 - 13 Dec 2024
Viewed by 479
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive disease of both upper motor neurons (UMNs) and lower motor neurons (LMNs) leading invariably to decline in motor function. The clinical exam is foundational to the diagnosis of the disease, and ordinal severity scales are used [...] Read more.
Amyotrophic lateral sclerosis (ALS) is a progressive disease of both upper motor neurons (UMNs) and lower motor neurons (LMNs) leading invariably to decline in motor function. The clinical exam is foundational to the diagnosis of the disease, and ordinal severity scales are used to track its progression. However, the lack of objective biomarkers of disease classification and progression delay clinical trial enrollment, muddle inclusion criteria, and limit accurate assessment of drug efficacy. Ultimately, biomarker evidence of therapeutic target engagement will support, and perhaps supplant, more traditional clinical trial outcome measures. Electrophysiology tools including nerve conduction study and electromyography (EMG) have already been established as diagnostic biomarkers of LMN degeneration in ALS. Additional understanding of the motor manifestations of disease is provided by motor unit number estimation, electrical impedance myography, and single-fiber EMG techniques. Dysfunction of UMN and non-motor brain areas is being increasingly assessed with transcranial magnetic stimulation, high-density electroencephalography, and magnetoencephalography; less common autonomic and sensory nervous system dysfunction in ALS can also be characterized. Although most of these techniques are used to explore the underlying disease mechanisms of ALS in research settings, they have the potential on a broader scale to noninvasively identify disease subtypes, predict progression rates, and assess physiologic engagement of experimental therapies. Full article
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<p>Relative percent drop of the motor unit number index (MUNIX) in a patient with ALS is greater and detected earlier than changes in the revised ALS functional rating scale (ALSFRS-R) score and slow vital capacity (SVC). (Modified and used with permission from reference [<a href="#B19-brainsci-14-01251" class="html-bibr">19</a>]).</p>
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<p>Transcranial magnetic stimulation excites a network of neurons in the underlying primary motor cortex (PMC) with a motor evoked potential (MEP) recorded over a contralateral intrinsic hand muscle (abductor pollicis brevis). (Modified and used with permission from reference [<a href="#B53-brainsci-14-01251" class="html-bibr">53</a>]).</p>
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24 pages, 3412 KiB  
Article
Effects of Different Transcranial Direct Current Stimulation Intensities over Dorsolateral Prefrontal Cortex on Brain Electrical Activity and Heart Rate Variability in Healthy and Fibromyalgia Women: A Randomized Crossover Trial
by Mari Carmen Gomez-Alvaro, Narcis Gusi, Ricardo Cano-Plasencia, Juan Luis Leon-Llamas, Alvaro Murillo-Garcia, Maria Melo-Alonso and Santos Villafaina
J. Clin. Med. 2024, 13(24), 7526; https://doi.org/10.3390/jcm13247526 - 11 Dec 2024
Viewed by 393
Abstract
People with fibromyalgia (FM) exhibit alterations in brain electrical activity and autonomic modulation compared to healthy individuals. Objectives: This study aimed to investigate transcranial direct current stimulation (tDCS) effects on brain electrocortical activity and heart rate variability (HRV), specifically targeting the dorsolateral [...] Read more.
People with fibromyalgia (FM) exhibit alterations in brain electrical activity and autonomic modulation compared to healthy individuals. Objectives: This study aimed to investigate transcranial direct current stimulation (tDCS) effects on brain electrocortical activity and heart rate variability (HRV), specifically targeting the dorsolateral prefrontal cortex in both healthy controls (HC) and FM groups, to identify potential differences in the responses between these groups, and to compare the effectiveness of two distinct tDCS intensities (1 mA and 2 mA) against a sham condition. Methods: Electroencephalography and electrocardiogram signals were recorded pre- and post-tDCS intervention. All participants underwent the three conditions (sham, 1 mA, and 2 mA) over three separate weeks, randomized in order. Results: No statistically significant baseline differences were found in the investigated HRV variables. In the FM group, 1 mA tDCS induced significant increases in LF, LF/HF, mean HR, SDNN, RMSSD, total power, SD1, SD2, and SampEn, and a decrease in HF, suggesting a shift toward sympathetic dominance. Additionally, 2 mA significantly increased SampEn compared to sham and 1 mA. In the HC group, sham increased DFA1 compared to 1 mA, and 2 mA induced smaller changes in SampEn relative to sham and 1 mA. No significant differences were found between FM and HC groups for any tDCS intensity. Conclusions: The effects of dlPFC-tDCS on HRV are intensity- and group-dependent, with the FM group exhibiting more pronounced changes at 1 mA and 2 mA. These findings emphasize the need for individualized stimulation protocols, given the variability in responses across groups and intensities. Full article
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<p>Theta power spectrum (4–7 Hz) topographic maps of the comparison of the effects of tDCS both between fibromyalgia and healthy groups and pre- post-tDCS intensities ((<b>A</b>): sham; (<b>B</b>): 1 mA; and (<b>C</b>): 2 mA) in each group separately. Statistically significant differences between pre- and post-tDCS intensities were not found in any of the healthy nor fibromyalgia groups independently. No differences in effects were observed between the fibromyalgia and healthy groups.</p>
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<p>Alpha power spectrum (8–12 Hz) topographic maps of the comparison of the effects of tDCS both between fibromyalgia and healthy groups and pre- and post-tDCS intensities ((<b>A</b>): sham; (<b>B</b>): 1 mA; and (<b>C</b>): 2 mA) in each group separately. Statistically significant differences between pre- and post-tDCS intensities were not found in any of the healthy nor fibromyalgia groups independently. No differences in effects were observed between the fibromyalgia and healthy groups.</p>
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<p>Beta power spectrum (13–30 Hz) topographic maps of the effects of tDCS both between fibromyalgia and healthy groups and pre- post-tDCS intensities ((<b>A</b>): sham; (<b>B</b>): 1 mA; and (<b>C</b>): 2 mA) in each group separately. Statistically significant differences in beta spectral power spectrum (<span class="html-italic">p</span> &lt; 0.05) were found in Cz location under 1 mA condition (<b>B</b>) for the healthy group, with higher values after the tDCS protocol. Differences in effects between groups were not detected.</p>
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<p>Theta (4–7 Hz)—(<b>1</b>), alpha (8–12 Hz)—(<b>2</b>), and y beta (13–30 Hz)—(<b>3</b>) power spectrum topographic maps of the comparison of the effects of tDCS between fibromyalgia and healthy groups and post-1 mA and 2 mA tDCS intensities. Statistically significant differences between the two intensities compared (1 mA and 2 mA) were found in the beta power spectrum in the healthy group for Cz location (<span class="html-italic">p</span> &lt; 0.05). Differences in effects between groups were detected in theta power spectrum for Fp1 location (<span class="html-italic">p</span> &lt; 0.05).</p>
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13 pages, 4259 KiB  
Review
Transcranial Magnetic Stimulation–Electroencephalography (TMS-EEG) in Neurosurgery: Unexplored Path Towards Personalized Brain Surgery
by Martim Oliveira, Sofia Ribeiro, Asfand Baig Mirza, Amisha Vastani, Alba Díaz-Baamonde, Masumi Tanaka, Ali Elhag, Francesco Marchi, Prajwal Ghimire, Feras Fayez, Sabina Patel, Richard Gullan, Ranjeev Bhangoo, Keyoumars Ashkan, Francesco Vergani, Ana Mirallave-Pescador and José Pedro Lavrador
J. Pers. Med. 2024, 14(12), 1144; https://doi.org/10.3390/jpm14121144 - 9 Dec 2024
Viewed by 763
Abstract
Background: Transcranial Magnetic Stimulation–Electroencephalography (TMS-EEG) is a non-operative technique that allows for magnetic cortical stimulation (TMS) and analysis of the electrical currents generated in the brain (EEG). Despite the regular utilization of both techniques independently, little is known about the potential impact of [...] Read more.
Background: Transcranial Magnetic Stimulation–Electroencephalography (TMS-EEG) is a non-operative technique that allows for magnetic cortical stimulation (TMS) and analysis of the electrical currents generated in the brain (EEG). Despite the regular utilization of both techniques independently, little is known about the potential impact of their combination in neurosurgical practice. Methods: This scoping review, conducted following PRISMA guidelines, focused on TMS-EEG in epilepsy, neuro-oncology, and general neurosurgery. A literature search in Embase and Ovid MEDLINE returned 3596 records, which were screened based on predefined inclusion and exclusion criteria. After full-text review, three studies met the inclusion criteria. Two independent investigators conducted study selection and data extraction, with mediators resolving disagreements. The NHLBI tool was used to assess risk of bias in the included studies. Results: A total of 3596 articles were screened following the above-mentioned criteria: two articles and one abstract met the inclusion criteria. TMS-EEG is mentioned as a promising tool to evaluate tumor–brain interaction, improve preoperative speech mapping, and for lateralization epileptic focus in patients undergoing epilepsy surgery. Lack of detailed patient and outcome information preclude further considerations about TMS-EEG use beyond the potential applications of this technique. Conclusions: TMS-EEG research in neurosurgery is required to establish the role of this non-invasive brain stimulation-recording technique. Tumor–brain interaction, preoperative mapping, and seizure lateralization are in the front row for its future applications. Full article
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<p>Schematic workflow for TMS-EEG concept—created with BioRender.com.</p>
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<p>PRISMA flowchart.</p>
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<p>Brain–tumor interface, focus epilepticus detection, and speech mapping—created with BioRender.com.</p>
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<p>Number of papers returned at each step of search. Please note the * (asterisk) in a search is used as a truncation symbol to find variations of a word that share the same root.</p>
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16 pages, 2647 KiB  
Article
Personalized Dose Selection for Treatment of Patients with Neuropsychiatric Disorders Using tDCS
by Sagarika Bhattacharjee, Rajan Kashyap, Vanteemar S. Sreeraj, Palanimuthu T. Sivakumar, Ganesan Venkatasubramanian, John E. Desmond, S. H. Annabel Chen, T. N. Sathyaprabha and Kaviraja Udupa
Brain Sci. 2024, 14(12), 1162; https://doi.org/10.3390/brainsci14121162 - 21 Nov 2024
Viewed by 828
Abstract
Background: Individualizing transcranial direct current stimulation (tDCS) parameters can improve precision in neuropsychiatric disorders. One important decision for the clinician is the selection of an appropriate montage—conventional or high-definition (HD)—to implement dose-controlled tDCS while maintaining the patient’s safety. Method: The present [...] Read more.
Background: Individualizing transcranial direct current stimulation (tDCS) parameters can improve precision in neuropsychiatric disorders. One important decision for the clinician is the selection of an appropriate montage—conventional or high-definition (HD)—to implement dose-controlled tDCS while maintaining the patient’s safety. Method: The present study simulated tDCS administration using T1-weighted brain images of 50 dementia, 25 depression patients, and 25 healthy individuals for two conventional and HD montages, targeting the regions of interest (ROIs) in the dorsal and ventral pathways that support language processing. For each tDCS configuration, the electric fields at the ROIs and the individualized dose required to achieve the desired current intensity at the target ROI across the subjects were estimated. Linear regression was performed on these parameters. Result: A significant relationship between atrophy and current dose that varies according to the disease was found. The dementia patients with significant brain atrophy required a higher personalized dosage for HD montage, as the current intensity at the target ROIs was lower and more variable than that of conventional montage. For dementia, tDCS individualization is pathway-dependent, wherein HD configuration of the dorsal route requires current dosages above the safety limit (>4 mA) for 46% of individuals. However, there was no significant difference in electrode configurations between the HD and traditional setups for depression and healthy volunteers without significant brain atrophy. Conclusions: HD-tDCS with fixed locations is limited, making conventional tDCS more effective for dose-controlled applications. In patients with atrophy, individualized adjustments based on simulations are needed due to the variable stimulation strength in the ROI. Full article
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<p>(<b>A</b>) Electric field simulation of tDCS dorsal and ventral pathway montages for both conventional and high-definition (HD) configurations demonstrating the (<b>i</b>) electrode positions, and (<b>ii</b>) electric field distributions across the brain regions. (<b>B</b>) Average current density (ACD) showing the electric field strength on the standard MNI brain (used as a reference for the calibration of doses) for conventional and HD configurations across (<b>i</b>) the dorsal pathway with two target ROIs: left inferior parietal lobule and left angular gyrus; and (<b>ii</b>) the ventral pathway with two target ROIs: left middle temporal gyrus and left inferior temporal gyrus. (<b>C</b>) Brain volumetric characteristics of the three groups (dementia, depression, and healthy) highlighting differences in the TBV [Total brain volume (GM + WM)] and TICV (Total intracranial volume (TBV + CSF)).</p>
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<p>The plot of mean ± standard deviations of ACD (<b>a</b>(<b>1</b>)–<b>a</b>(<b>4</b>)) highlighting their distribution across the subjects (<b>b</b>(<b>1</b>)–<b>b</b>(<b>4</b>)) for dorsal and ventral ROIs using conventional and high-definition montages for dementia, depression, and healthy volunteers. Level of significance denoted by * &lt;0.05. ** &lt;0.01, *** &lt;0.001.</p>
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<p>The plot of mean ± standard deviations of personalized dosages (<b>a</b>(<b>1</b>)–<b>a</b>(<b>4</b>)) highlighting their distribution across the subjects (<b>b</b>(<b>1</b>)–<b>b</b>(<b>4</b>)) for dorsal and ventral ROIs using conventional and high-definition montages for dementia, depression, and healthy volunteers. Level of significance denoted by ** &lt;0.01.</p>
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<p>Showing (<b>i</b>) The significant (<span class="html-italic">p</span> &lt; 0.001) relationship between the atrophy parameter and personalized doses at the target ROI at the left inferior parietal lobule, and (<b>ii</b>) its significant variation across the three groups: depression, dementia, and healthy volunteers. (*** denotes significance level &lt; 0.001).</p>
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22 pages, 6422 KiB  
Systematic Review
Metaanalysis of Repetitive Transcranial Magnetic Stimulation (rTMS) Efficacy for OCD Treatment: The Impact of Stimulation Parameters, Symptom Subtype and rTMS-Induced Electrical Field
by Fateme Dehghani-Arani, Reza Kazemi, Amir-Homayun Hallajian, Sepehr Sima, Samaneh Boutimaz, Sepideh Hedayati, Saba Koushamoghadam, Razieh Safarifard and Mohammad Ali Salehinejad
J. Clin. Med. 2024, 13(18), 5358; https://doi.org/10.3390/jcm13185358 - 10 Sep 2024
Viewed by 2030
Abstract
Background: Repetitive transcranial magnetic stimulation (rTMS) has recently demonstrated significant potential in treating obsessive-compulsive disorder (OCD). However, its effectiveness depends on various parameters, including stimulation parameters, OCD subtypes and electrical fields (EFs) induced by rTMS in targeted brain regions that are less [...] Read more.
Background: Repetitive transcranial magnetic stimulation (rTMS) has recently demonstrated significant potential in treating obsessive-compulsive disorder (OCD). However, its effectiveness depends on various parameters, including stimulation parameters, OCD subtypes and electrical fields (EFs) induced by rTMS in targeted brain regions that are less studied. Methods: Using the PRISMA approach, we examined 27 randomized control trials (RCTs) conducted from 1985 to 2024 using rTMS for the treatment of OCD and conducted several meta-analyses to investigate the role of rTMS parameters, including the EFs induced by each rTMS protocol, and OCD subtypes on treatment efficacy. Results: A significant, medium effect size was found, favoring active rTMS (gPPC = 0.59, p < 0.0001), which was larger for the obsession subscale. Both supplementary motor area (SMA) rTMS (gPPC = 0.82, p = 0.048) and bilateral dorsolateral prefrontal cortex (DLPFC) rTMS (gPPC = 1.14, p = 0.04) demonstrated large effect sizes, while the right DLPFC showed a significant moderate effect size for reducing OCD severity (gPPC = 0.63, p = 0.012). These protocols induced the largest EFs in dorsal cognitive, ventral cognitive and sensorimotor circuits. rTMS protocols targeting DLPFC produced the strongest electrical fields in cognitive circuits, while pre-supplementary motor area (pre-SMA) and orbitofrontal cortex (OFC) rTMS protocols induced larger fields in regions linked to emotional and affective processing in addition to cognitive circuits. The pre-SMA rTMS modulated more circuits involved in OCD pathophysiology—sensorimotor, cognitive, affective, and frontolimbic—with larger electrical fields than the other protocols. Conclusions: While rTMS shows moderate overall clinical efficacy, protocols targeting ventral and dorsal cognitive and sensorimotor circuits demonstrate the highest potential. The pre-SMA rTMS appears to induce electrical fields in more circuits relevant to OCD pathophysiology. Full article
(This article belongs to the Special Issue Neuro-Psychiatric Disorders: Updates on Diagnosis and Treatment)
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<p>Flowchart diagram of the study selection process.</p>
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<p>The distribution of the induced normed electrical field for each rTMS protocol.</p>
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<p>(<b>A</b>) Pooled effect sizes (g<sub>ppc</sub>) of rTMS studies for reducing OCD symptoms. (<b>B</b>) Pooled effect sizes (gppc) of rTMS studies for reducing obsession symptoms (<b>C</b>) Pooled effect sizes (g<sub>ppc</sub>) of rTMS studies for reducing compulsion symptoms, CI: confidence interval, SMD: standardized mean difference [<a href="#B54-jcm-13-05358" class="html-bibr">54</a>,<a href="#B55-jcm-13-05358" class="html-bibr">55</a>,<a href="#B56-jcm-13-05358" class="html-bibr">56</a>,<a href="#B57-jcm-13-05358" class="html-bibr">57</a>,<a href="#B58-jcm-13-05358" class="html-bibr">58</a>,<a href="#B59-jcm-13-05358" class="html-bibr">59</a>,<a href="#B60-jcm-13-05358" class="html-bibr">60</a>,<a href="#B61-jcm-13-05358" class="html-bibr">61</a>,<a href="#B62-jcm-13-05358" class="html-bibr">62</a>,<a href="#B63-jcm-13-05358" class="html-bibr">63</a>,<a href="#B64-jcm-13-05358" class="html-bibr">64</a>,<a href="#B65-jcm-13-05358" class="html-bibr">65</a>,<a href="#B66-jcm-13-05358" class="html-bibr">66</a>,<a href="#B67-jcm-13-05358" class="html-bibr">67</a>,<a href="#B68-jcm-13-05358" class="html-bibr">68</a>,<a href="#B69-jcm-13-05358" class="html-bibr">69</a>,<a href="#B70-jcm-13-05358" class="html-bibr">70</a>,<a href="#B71-jcm-13-05358" class="html-bibr">71</a>,<a href="#B72-jcm-13-05358" class="html-bibr">72</a>,<a href="#B73-jcm-13-05358" class="html-bibr">73</a>,<a href="#B74-jcm-13-05358" class="html-bibr">74</a>,<a href="#B75-jcm-13-05358" class="html-bibr">75</a>,<a href="#B76-jcm-13-05358" class="html-bibr">76</a>,<a href="#B77-jcm-13-05358" class="html-bibr">77</a>,<a href="#B78-jcm-13-05358" class="html-bibr">78</a>,<a href="#B79-jcm-13-05358" class="html-bibr">79</a>,<a href="#B80-jcm-13-05358" class="html-bibr">80</a>].</p>
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<p>(<b>A</b>) Pooled effect sizes (gppc) of rTMS studies for reducing OCD symptoms based on the cortical target of rTMS, BL: bilateral, L: left, R: right, DLPFC: dorsolateral prefrontal cortex, OFC: orbitofrontal cortex, SMA: supplementary motor area. (<b>B</b>) Effect sizes (gppc) for OCD symptoms based on the frequency of rTMS. (<b>C</b>) Pooled effect sizes (gppc) for OCD symptoms based on the duration of rTMS treatment. (<b>D</b>) Pooled effect sizes (gppc) for OCD symptoms based on the total induced pulses of rTMS per session, TTPS: total pulse per session. (<b>E</b>) Effect sizes (gppc) for OCD symptoms based on the intensity of rTMS, RMT: resting motor threshold, CI: confidence interval, SMD: standardized mean difference [<a href="#B54-jcm-13-05358" class="html-bibr">54</a>,<a href="#B55-jcm-13-05358" class="html-bibr">55</a>,<a href="#B56-jcm-13-05358" class="html-bibr">56</a>,<a href="#B57-jcm-13-05358" class="html-bibr">57</a>,<a href="#B58-jcm-13-05358" class="html-bibr">58</a>,<a href="#B59-jcm-13-05358" class="html-bibr">59</a>,<a href="#B60-jcm-13-05358" class="html-bibr">60</a>,<a href="#B61-jcm-13-05358" class="html-bibr">61</a>,<a href="#B62-jcm-13-05358" class="html-bibr">62</a>,<a href="#B63-jcm-13-05358" class="html-bibr">63</a>,<a href="#B64-jcm-13-05358" class="html-bibr">64</a>,<a href="#B65-jcm-13-05358" class="html-bibr">65</a>,<a href="#B66-jcm-13-05358" class="html-bibr">66</a>,<a href="#B67-jcm-13-05358" class="html-bibr">67</a>,<a href="#B68-jcm-13-05358" class="html-bibr">68</a>,<a href="#B69-jcm-13-05358" class="html-bibr">69</a>,<a href="#B70-jcm-13-05358" class="html-bibr">70</a>,<a href="#B71-jcm-13-05358" class="html-bibr">71</a>,<a href="#B72-jcm-13-05358" class="html-bibr">72</a>,<a href="#B73-jcm-13-05358" class="html-bibr">73</a>,<a href="#B74-jcm-13-05358" class="html-bibr">74</a>,<a href="#B75-jcm-13-05358" class="html-bibr">75</a>,<a href="#B76-jcm-13-05358" class="html-bibr">76</a>,<a href="#B77-jcm-13-05358" class="html-bibr">77</a>,<a href="#B78-jcm-13-05358" class="html-bibr">78</a>,<a href="#B79-jcm-13-05358" class="html-bibr">79</a>,<a href="#B80-jcm-13-05358" class="html-bibr">80</a>].</p>
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<p>(<b>A</b>) Effect sizes (gppc) for OCD symptoms based on the presence of MDD comorbidity. (<b>B</b>) Effect sizes (gppc) for OCD symptoms based on the strategy of rTMS treatment. (<b>C</b>) Effect sizes (gppc) for OCD symptoms based on the sham stimulation strategy. (<b>D</b>) Effect sizes (gppc) for OCD symptoms based on the blinding strategy. MDD: major depressive disorder, CI: confidence interval, SMD: standardized mean difference [<a href="#B54-jcm-13-05358" class="html-bibr">54</a>,<a href="#B55-jcm-13-05358" class="html-bibr">55</a>,<a href="#B56-jcm-13-05358" class="html-bibr">56</a>,<a href="#B57-jcm-13-05358" class="html-bibr">57</a>,<a href="#B58-jcm-13-05358" class="html-bibr">58</a>,<a href="#B59-jcm-13-05358" class="html-bibr">59</a>,<a href="#B60-jcm-13-05358" class="html-bibr">60</a>,<a href="#B61-jcm-13-05358" class="html-bibr">61</a>,<a href="#B62-jcm-13-05358" class="html-bibr">62</a>,<a href="#B63-jcm-13-05358" class="html-bibr">63</a>,<a href="#B64-jcm-13-05358" class="html-bibr">64</a>,<a href="#B65-jcm-13-05358" class="html-bibr">65</a>,<a href="#B66-jcm-13-05358" class="html-bibr">66</a>,<a href="#B67-jcm-13-05358" class="html-bibr">67</a>,<a href="#B68-jcm-13-05358" class="html-bibr">68</a>,<a href="#B69-jcm-13-05358" class="html-bibr">69</a>,<a href="#B70-jcm-13-05358" class="html-bibr">70</a>,<a href="#B71-jcm-13-05358" class="html-bibr">71</a>,<a href="#B72-jcm-13-05358" class="html-bibr">72</a>,<a href="#B73-jcm-13-05358" class="html-bibr">73</a>,<a href="#B74-jcm-13-05358" class="html-bibr">74</a>,<a href="#B75-jcm-13-05358" class="html-bibr">75</a>,<a href="#B76-jcm-13-05358" class="html-bibr">76</a>,<a href="#B77-jcm-13-05358" class="html-bibr">77</a>,<a href="#B78-jcm-13-05358" class="html-bibr">78</a>,<a href="#B79-jcm-13-05358" class="html-bibr">79</a>,<a href="#B80-jcm-13-05358" class="html-bibr">80</a>].</p>
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<p>(<b>A</b>) Bar plot showing the distribution of risk-of-bias judgments across bias domains. The bars indicate the proportion of studies within each domain, providing an overview of the collective bias risk. The colors represent: low risk (green), some concerns (yellow), and high risk (red). (<b>B</b>) GRADE assessment results. *: Lack of Intention-to-treat analysis in several studies; many didn’t report the allocation concealment procedure (Only 6 studies had done and intention-to-treat analysis 4 of which are in the SMA/pre-SMA group). In addition, the funnel plot shows an asymmetrical pattern suggesting the presence of publication bias. **: 95% CI has broad intervals or/and includes both significant benefit of treatment and notable harm.</p>
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13 pages, 1058 KiB  
Article
Preoperative Cortical Mapping for Brain Tumor Surgery Using Navigated Transcranial Stimulation: Analysis of Accuracy
by Wellingson Silva Paiva, Erich Talamoni Fonoff, Rhuann Pontes dos Santos Silva, Lucas Schiavao, André Russowsky Brunoni, César Cimonari de Almeida and Carlos Carlotti Júnior
Brain Sci. 2024, 14(9), 867; https://doi.org/10.3390/brainsci14090867 - 28 Aug 2024
Viewed by 698
Abstract
Transcranial magnetic stimulation (TMS) represents a distinctive technique for non-invasive brain stimulation. Recent advancements in image processing have enabled the enhancement of TMS by integrating magnetic resonance imaging (MRI) modalities with TMS via a neuronavigation system. The aim of this study is to [...] Read more.
Transcranial magnetic stimulation (TMS) represents a distinctive technique for non-invasive brain stimulation. Recent advancements in image processing have enabled the enhancement of TMS by integrating magnetic resonance imaging (MRI) modalities with TMS via a neuronavigation system. The aim of this study is to assess the efficacy of navigated TMS for cortical mapping in comparison to surgical mapping using direct electrical stimulation (DES). This study involved 30 neurosurgical procedures for tumors located in or adjacent to the precentral gyrus. The DES points were compared with TMS responses based on the original distances of vectorial modules. There was a notable similarity in the points obtained from the two mapping methods. The distances between the geometric centers of TMS and DCS were 4.85 ± 1.89 mm. A strong correlation was identified between these vectorial points (r = 0.901, p < 0.001). The motor threshold in TMS was highest in the motor cortex adjacent to the tumor compared to the normal cortex (p < 0.001). Patients with deficits exhibited excellent accuracy in both methods. In view of this, TMS demonstrated reliable and precise application in brain mapping, which is a promising method for preoperative functional mapping in motor cortex tumor surgery. Full article
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<p>Correlation study between resting motor thresholds in nTMS, with the X-axis representing % of maximum generator activity, and the Y-axis representing threshold in DES (mA). nTMS: navigated transcranial magnetic stimulation; DES: direct electrical stimulation.</p>
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<p>Bland–Altman distribution in the X (<b>A</b>), Y (<b>B</b>) Z (<b>C</b>) coordinates and vector magnitudes (<b>D</b>) between the two mapping methods. The X-axis displays the mean vector magnitude values for each patient. The Y-axis displays the vector difference for the vector magnitudes in nTMS comparing to DES. nTMS: navigated transcranial magnetic stimulation. DCS: Direct Cortical Stimulation.</p>
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<p>Three-dimensional MRI with points obtained in motor response in TMS (yellow octahedrons), and motor response recorded in the DES mapping (red circles) during the surgery. Fusion with intraoperative imaging. DES: direct electrical stimulation; MRI: magnetic resonance imaging; TMS: transcranial magnetic stimulation.</p>
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12 pages, 4094 KiB  
Article
High-Frequency Magnetic Pulse Generator for Low-Intensity Transcranial Magnetic Stimulation
by Seungjae Shin, Hyungeun Kim and Jinho Jeong
Electronics 2024, 13(16), 3160; https://doi.org/10.3390/electronics13163160 - 10 Aug 2024
Viewed by 1199
Abstract
This paper presents a high-frequency (HF) magnetic pulse generator designed for low-intensity transcranial magnetic stimulation (LI-TMS) applications. HF pulse stimulation can induce a strong electric field with minimal current and enhance the penetration depth of the electric field in human tissue. The HF [...] Read more.
This paper presents a high-frequency (HF) magnetic pulse generator designed for low-intensity transcranial magnetic stimulation (LI-TMS) applications. HF pulse stimulation can induce a strong electric field with minimal current and enhance the penetration depth of the electric field in human tissue. The HF magnetic pulse generator was designed and fabricated using a microcontroller unit, gate driver, full-bridge coil driver, and stimulation coil. Measurements with a full-bridge circuit supply voltage of 10 V demonstrated an electric field intensity of 6.8 Vpp/m at a frequency of 1 MHz with a power dissipation of 2.45 W. Achieving a similar electric field intensity at a frequency of 100 kHz required approximately ten times the coil current. Additionally, a quasi-resonant LC load was introduced by connecting a capacitor in series with the stimulation coil, which set the resonant frequency to approximately 10% higher than the frequency of 1 MHz. This approach reduced the coil impedance, achieving higher current with the same bias supply voltage. Experimental results showed an enhanced electric field intensity of 19.1 Vpp/m with a supply voltage of only 1.8 V and reduced power dissipation of 1.11 W. The proposed HF pulse train with quasi-resonant coil system is expected to enable a low-power LI-TMS system. Full article
(This article belongs to the Section Circuit and Signal Processing)
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<p>Basic principles of the TMS system.</p>
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<p>Stimulation protocols. (<b>a</b>) Traditional theta burst stimulation. (<b>b</b>) Proposed high-frequency stimulation.</p>
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<p>Simulation of the electric field intensity in human gray matter. (<b>a</b>) Simulation structure. (<b>b</b>) Normalized <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> as a function of frequency. (<b>c</b>) Penetration depth of the electric field as a function of frequency.</p>
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<p>Block diagram of the proposed HF magnetic pulse generator.</p>
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<p>Schematic of the full-bridge circuit with boot-strapped gate driver.</p>
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<p>Simulated waveforms for the control signal (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>C</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>–<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>C</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math>), the coil current (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>), and the voltage across the coil (<math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>). (<b>a</b>) Triangular current wave. (<b>b</b>) Trapezoidal current wave.</p>
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<p>Simulated waveforms in the full-bridge circuit with (solid) and without (dot) bypass capacitor and damping resistor for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>D</mi> <mo>,</mo> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math> = 10 V and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>D</mi> <mo>,</mo> <mi>G</mi> <mi>D</mi> </mrow> </msub> </mrow> </semantics></math> = 5.5 V. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>D</mi> <mo>,</mo> <mi>F</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>G</mi> <mi>S</mi> <mo>,</mo> <mi>U</mi> </mrow> </msub> </mrow> </semantics></math>, and (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> at 100 kHz. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>D</mi> <mo>,</mo> <mi>F</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>G</mi> <mi>S</mi> <mo>,</mo> <mi>U</mi> </mrow> </msub> </mrow> </semantics></math>, and (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> at 1 MHz.</p>
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<p>(<b>a</b>) Fabricated HF magnetic pulse generator. (<b>b</b>) Fabricated stimulation coil.</p>
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<p>Measurement setup for the induced electric field intensity.</p>
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<p>Measured waveforms for the inductor load with DC bias voltages, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>D</mi> <mo>,</mo> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math> = 10 V and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>D</mi> <mo>,</mo> <mi>G</mi> <mi>D</mi> </mrow> </msub> </mrow> </semantics></math> = 5.5 V. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math> at 100 kHz. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math> at 1 MHz.</p>
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<p>Measured waveforms for the quasi-resonant LC load at the frequency of 1 MHz with DC bias voltages, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>D</mi> <mo>,</mo> <mi>F</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math> 1.8 V and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>D</mi> <mo>,</mo> <mi>G</mi> <mi>D</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math> 5.5 V. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>L</mi> <mi>C</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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20 pages, 7240 KiB  
Article
Investigating the Effects of Transcranial Alternating Current Stimulation on Cortical Oscillations and Network Dynamics
by Sandeep Kumar Agnihotri and Jiang Cai
Brain Sci. 2024, 14(8), 767; https://doi.org/10.3390/brainsci14080767 - 29 Jul 2024
Cited by 1 | Viewed by 1379
Abstract
Transcranial electrical brain stimulation techniques like transcranial direct current (tDCS) and transcranial alternating current (tACS) have emerged as potential tools for treating neurological diseases by modulating cortical excitability. These techniques deliver small electric currents to the brain non-invasively through electrodes on the scalp. [...] Read more.
Transcranial electrical brain stimulation techniques like transcranial direct current (tDCS) and transcranial alternating current (tACS) have emerged as potential tools for treating neurological diseases by modulating cortical excitability. These techniques deliver small electric currents to the brain non-invasively through electrodes on the scalp. tDCS uses constant direct current which weakly alters the membrane voltage of cortical neurons, while tACS utilizes alternating current to target and enhance cortical oscillations, though the underlying mechanisms are not fully understood more specifically. To elucidate how tACS perturbs endogenous network dynamics, we simulated spiking neuron network models. We identified distinct roles of the depolarizing and hyperpolarizing phases in driving network activity towards and away from the strong nonlinearity provided by pyramidal neurons. Exploring resonance effects, we found matching tACS frequency to the network’s endogenous resonance frequency creates greater entrainment. Based on this, we developed an algorithm to determine the network’s endogenous frequency, phase, and amplitude, then deliver optimized tACS to entrain network oscillations. Together, these computational results provide mechanistic insight into the effects of tACS on network dynamics and could inform future closed-loop tACS systems that dynamically tune stimulation parameters to ongoing brain activity. Full article
(This article belongs to the Section Neurotechnology and Neuroimaging)
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<p>Two-dimensional cortical network of computational model. (<b>A</b>) Neural network connectivity between excitatory neurons and inhibitory neurons with excitatory neurons (red) in a 40 × 40 matrix and inhibitory neurons (blue) on the 20 × 20 matrix. (<b>B</b>) Connections between excitatory-to-excitatory neurons and their synaptic connections between 1600 neurons. (<b>C</b>) Connections between excitatory neurons and inhibitory neurons, where the x axis shows the excitatory neurons and the y axis indicates inhibitory neurons in the computational model.</p>
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<p>Global oscillatory activity in cortical network of computational model. (<b>A</b>) Spike raster plot of network activity with neural spikes, where excitatory and inhibitory neurons synchronize and show network activity (UP states: red (excitatory neurons) and blue (inhibitory neurons) and quiescence (DOWN states: white). (<b>B</b>) Membrane voltage traces of excitatory neurons, inhibitory neurons, and spikes of neuros, indicate that neurons activity coordinates with the network. (<b>C</b>) Percentage of spiking excitatory and inhibitory neurons during network activity and explicit synchronization. (<b>D</b>) Plot showing the temporal activity patterns of EX and IN percentage of neurons over a network activity.</p>
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<p>Network activity of excitatory and inhibitory neurons. (<b>A</b>) Percentage of excitatory spiking neurons during network activity. (<b>B</b>) Percentage of inhibitory neurons during network activity of computational model. (<b>C</b>) Percentage of spiking excitatory and inhibitory neurons during network activity and explicit synchronization. (<b>D</b>) power spectral density (PSD) graph showing that the maximum frequency is 4 Hz of the network. (<b>E</b>) Represents the power/frequency (dB/Hz) against the frequency, where 4 Hz frequency has the maximum power.</p>
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<p>Excitatory neuronal activity during a network UP state. (<b>A</b>) Two-dimensional map of excitatory neurons in UP state of network at t = 0, 30, 60,90 and 120 ms, membrane voltage increased with time duration as network activity increased and expanded. (<b>B</b>) Spike raster plot maintains the same firings of excitatory and inhibitory neurons in the UP and DOWN state synchronously in the computational model. The neurons fire according to the color code above and are indicated by warmer colors.</p>
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<p>Comparison of tDCS and tACS stimulation. (<b>A</b>) Overall network activity of spiking neurons, percentage of excitatory neurons, and percentage of inhibitory neurons when the stimulation of waveform 4 Hz is applied, approximately matched with intrinsic network oscillations with the same amplitude (5 pA), (<b>B</b>) t-ACS easily entertained the network and increased the network activity along with the number of excitatory neurons, but tDCS failed to entrain with the network oscillations.</p>
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<p>Applying the depolarizing-only stimulation and hyperpolarizing-only stimulation. (<b>A</b>) Network activity of spiking neurons, excitatory neurons, and inhibitory neurons after stimulation using only depolarizing-only waveform; networks were entrained with stimulation frequency but were not regular. (<b>B</b>) Stimulation of hyperpolarizing-only waveforms exhibits stable entrainment after a few milliseconds with the network and spiking neurons networks and the percentage of excitatory neurons is higher after entrainment.</p>
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<p>Scatter plot demonstrates a series of t-ACS stimulations, conducted with varying amplitudes (1, 5, 10, and 15) and 0–9 frequencies. The excitatory network’s activity demonstrates resonance with the network under all stimulations. Notably, at lower amplitude (5 pA), resonance occurs at the maximum level with the intrinsic frequency. However, at a higher amplitude, resonance with a 7 Hz frequency is observed. While a range of frequencies entertain the network under all amplitudes, higher amplitudes struggle to maintain sustained entrainment across different frequencies.</p>
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<p>The algorithm is run with same phase, frequency, and amplitude along with the network oscillations. (<b>A</b>) algorithm identified the phase, frequency and amplitude of the networks and generate the same waveform to entrain with the network and spiking neurons and excitatory neuron networks with average relative power 0.98. (<b>B</b>,<b>C</b>) Spectrogram of stimulated frequency matched with the networks oscillations frequency and (<b>D</b>) show power of lower frequency band after stimulation with frequency.</p>
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21 pages, 2818 KiB  
Systematic Review
Neuromodulation Treatments Targeting Pathological Synchrony for Tinnitus in Adults: A Systematic Review
by Derek J. Hoare, Gillian W. Shorter, Giriraj S. Shekhawat, Amr El Refaie, Bas Labree and Magdalena Sereda
Brain Sci. 2024, 14(8), 748; https://doi.org/10.3390/brainsci14080748 - 26 Jul 2024
Viewed by 1884
Abstract
(1) Background: Tinnitus involves the conscious awareness of a tonal or composite noise for which there is no identifiable corresponding external acoustic source. For many people, tinnitus is a disorder associated with symptoms of emotional distress, cognitive dysfunction, autonomic arousal, behavioural changes, and [...] Read more.
(1) Background: Tinnitus involves the conscious awareness of a tonal or composite noise for which there is no identifiable corresponding external acoustic source. For many people, tinnitus is a disorder associated with symptoms of emotional distress, cognitive dysfunction, autonomic arousal, behavioural changes, and functional disability. Many symptoms can be addressed effectively using education or cognitive behavioural therapy. However, there is no treatment that effectively reduces or alters tinnitus-related neurophysiological activity and thus the tinnitus percept. In this systematic review, we evaluated the effectiveness of neuromodulation therapies for tinnitus that explicitly target pathological synchronous neural activity. (2) Methods: Multiple databases were searched for randomised controlled trials of neuromodulation interventions for tinnitus in adults, with 24 trials included. The risk of bias was assessed, and where appropriate, meta-analyses were performed. (3) Results: Few trials used acoustic, vagal nerve, or transcranial alternating current stimulation, or bimodal stimulation techniques, with limited evidence of neuromodulation or clinical effectiveness. Multiple trials of transcranial direct current stimulation (tDCS) were identified, and a synthesis demonstrated a significant improvement in tinnitus symptom severity in favour of tDCS versus control, although heterogeneity was high. (4) Discussion: Neuromodulation for tinnitus is an emerging but promising field. Electrical stimulation techniques are particularly interesting, given recent advances in current flow modelling that can be applied to future studies. Full article
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<p>PRISMA chart showing flow from record identification to final inclusion in review of neuromodulation treatments for tinnitus.</p>
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<p>Risk of bias summary for review of neuromodulation treatments for tinnitus [<a href="#B28-brainsci-14-00748" class="html-bibr">28</a>,<a href="#B29-brainsci-14-00748" class="html-bibr">29</a>,<a href="#B30-brainsci-14-00748" class="html-bibr">30</a>,<a href="#B31-brainsci-14-00748" class="html-bibr">31</a>,<a href="#B32-brainsci-14-00748" class="html-bibr">32</a>,<a href="#B33-brainsci-14-00748" class="html-bibr">33</a>,<a href="#B34-brainsci-14-00748" class="html-bibr">34</a>,<a href="#B35-brainsci-14-00748" class="html-bibr">35</a>,<a href="#B36-brainsci-14-00748" class="html-bibr">36</a>,<a href="#B37-brainsci-14-00748" class="html-bibr">37</a>,<a href="#B38-brainsci-14-00748" class="html-bibr">38</a>,<a href="#B39-brainsci-14-00748" class="html-bibr">39</a>,<a href="#B40-brainsci-14-00748" class="html-bibr">40</a>,<a href="#B41-brainsci-14-00748" class="html-bibr">41</a>,<a href="#B42-brainsci-14-00748" class="html-bibr">42</a>,<a href="#B43-brainsci-14-00748" class="html-bibr">43</a>,<a href="#B44-brainsci-14-00748" class="html-bibr">44</a>,<a href="#B45-brainsci-14-00748" class="html-bibr">45</a>,<a href="#B46-brainsci-14-00748" class="html-bibr">46</a>,<a href="#B47-brainsci-14-00748" class="html-bibr">47</a>,<a href="#B48-brainsci-14-00748" class="html-bibr">48</a>,<a href="#B49-brainsci-14-00748" class="html-bibr">49</a>,<a href="#B50-brainsci-14-00748" class="html-bibr">50</a>,<a href="#B51-brainsci-14-00748" class="html-bibr">51</a>].</p>
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<p>Forest plot of acoustic CR neuromodulation versus placebo effects on tinnitus symptom severity [<a href="#B35-brainsci-14-00748" class="html-bibr">35</a>,<a href="#B45-brainsci-14-00748" class="html-bibr">45</a>]. Green box indicates relative size of sample and mean effect. Diamond indicates pooled effect.</p>
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<p>Change in oscillatory power in delta brainwave patterns as measured by electroencephalography in acoustic CR neuromodulation versus placebo control [<a href="#B35-brainsci-14-00748" class="html-bibr">35</a>,<a href="#B45-brainsci-14-00748" class="html-bibr">45</a>]. Green box indicates relative size of sample and mean effect. Diamond indicates pooled effect.</p>
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<p>Forest plot comparing effects of multiple sessions of transcranial direct current stimulation versus sham stimulation on tinnitus symptom severity [<a href="#B29-brainsci-14-00748" class="html-bibr">29</a>,<a href="#B30-brainsci-14-00748" class="html-bibr">30</a>,<a href="#B31-brainsci-14-00748" class="html-bibr">31</a>,<a href="#B33-brainsci-14-00748" class="html-bibr">33</a>,<a href="#B38-brainsci-14-00748" class="html-bibr">38</a>,<a href="#B40-brainsci-14-00748" class="html-bibr">40</a>,<a href="#B41-brainsci-14-00748" class="html-bibr">41</a>,<a href="#B48-brainsci-14-00748" class="html-bibr">48</a>,<a href="#B50-brainsci-14-00748" class="html-bibr">50</a>]. Green box indicates relative size of sample and mean effect. Diamond indicates pooled effect.</p>
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<p>Forest plot comparing change in generalised anxiety post-transcranial direct current stimulation versus sham control [<a href="#B29-brainsci-14-00748" class="html-bibr">29</a>,<a href="#B38-brainsci-14-00748" class="html-bibr">38</a>,<a href="#B48-brainsci-14-00748" class="html-bibr">48</a>,<a href="#B50-brainsci-14-00748" class="html-bibr">50</a>]. Green box indicates relative size of sample and mean effect. Diamond indicates pooled effect.</p>
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<p>Forest plot comparing change in generalised depression post-transcranial direct current stimulation versus sham control [<a href="#B29-brainsci-14-00748" class="html-bibr">29</a>,<a href="#B38-brainsci-14-00748" class="html-bibr">38</a>,<a href="#B48-brainsci-14-00748" class="html-bibr">48</a>,<a href="#B50-brainsci-14-00748" class="html-bibr">50</a>]. Green box indicates relative size of sample and mean effect. Diamond indicates pooled effect.</p>
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12 pages, 1678 KiB  
Article
Quantitative Analysis of the Effect of Neuromuscular Blockade on Motor-Evoked Potentials in Patients Undergoing Brain Tumor Removal Surgery: A Prospective, Single-Arm, Open-Label Observational Study
by Dongwoo Chae, Hyun-Chang Kim, Hun Ho Park, Jihwan Yoo, Yoon Ghil Park, Kyu Wan Kwak, Dawoon Kim, Jinyoung Park and Dong Woo Han
J. Clin. Med. 2024, 13(15), 4281; https://doi.org/10.3390/jcm13154281 - 23 Jul 2024
Viewed by 951
Abstract
Background: We aimed to elucidate the quantitative relationship between the neuromuscular blockade depth and intraoperative motor-evoked potential amplitudes. Methods: This prospective, single-arm, open-label, observational study was conducted at a single university hospital in Seoul, Korea, and included 100 adult patients aged [...] Read more.
Background: We aimed to elucidate the quantitative relationship between the neuromuscular blockade depth and intraoperative motor-evoked potential amplitudes. Methods: This prospective, single-arm, open-label, observational study was conducted at a single university hospital in Seoul, Korea, and included 100 adult patients aged ≥19 years undergoing brain tumor removal surgery under general anesthesia. We measured the neuromuscular blockade degree and motor-evoked potential amplitude in the deltoid, abductor pollicis brevis, tibialis anterior, and abductor hallucis muscles until dural opening. Results: The pharmacokinetic-pharmacodynamic model revealed the exposure-response relationship between the rocuronium effect-site concentration and motor-evoked potential amplitudes. The mean motor-evoked potential amplitudes decreased proportionally with increasing neuromuscular blockade depth. As the mean amplitude increased, the coefficient of variation decreased bi-exponentially. The critical ratio of the first evoked response to the train-of-four stimulation (T1)/control response (Tc) thresholds beyond which the coefficient of variation exhibited minimal change were found to be 0.63, 0.65, 0.68, and 0.63 for the deltoid, abductor pollicis brevis, tibialis anterior, and abductor hallucis muscles, respectively. Conclusions: Our results reveal that the motor-evoked potential amplitude exhibits deterioration proportional to the degree of neuromuscular blockade. In light of the observed bi-exponential decline of the coefficient of variation with the motor-evoked potential amplitude, we recommend maintaining a T1/Tc ratio higher than 0.6 for partial neuromuscular blockade. Full article
(This article belongs to the Special Issue Advances in the Clinical Management of Perioperative Anesthesia)
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<p>(<b>A</b>) The recovery trajectory of the T1/Tc and μ. (<b>B</b>) The recovery trajectories of the T1/Tc and μ of the four muscles. (<b>C</b>) The quantitative relationship between the μ and T1/Tc. (<b>D</b>) Distributional characteristics of μ for the four muscles conditioned on different T1/Tc intervals. (<b>E</b>) Individual-level relationships between μ and T1/Tc in four different individuals. T1, first evoked response to train-of-four stimulation; Tc, control response before rocuronium infusion; MEP, motor-evoked potential; Del, deltoid; APB, abductor pollicis brevis; TA, tibialis anterior; AH, abductor hallucis; µ, mean MEP amplitude.</p>
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<p>Observed and predicted values of the T1/Tc (<b>A</b>) and MEP amplitude (<b>B</b>). Measurements acquired from different sensors were distinguished using different colors. (<b>C</b>) Observed (dot) and predicted (line) MEPs overlaid in a randomly selected patient, with different colors representing measurements from different recording electrodes. T1, first evoked response to train-of-four stimulation; Tc, control response before rocuronium infusion; MEP, motor-evoked potential; Del, deltoid; APB, abductor pollicis brevis; TA, tibialis anterior; AH, abductor hallucis.</p>
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<p>(<b>A</b>) The CV of the MEP amplitude is inversely correlated with the mean MEP amplitude. (<b>B</b>) Observed versus predicted standard deviations in the bi-exponential model. CV, coefficient of variance; Del, deltoid; APB, abductor pollicis brevis; TA, tibialis anterior; AH, abductor hallucis; MEP, motor-evoked potential.</p>
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15 pages, 5385 KiB  
Article
Evaluating Commercial Electrical Neuromodulation Devices with Low-Cost Neural Phantoms
by John LaRocco, Taeyoon Eom, Ekansh Seth, Vania Gandhi, Anna Bontempo and Eric Zachariah
Appl. Sci. 2024, 14(14), 6328; https://doi.org/10.3390/app14146328 - 20 Jul 2024
Viewed by 2615
Abstract
Non-invasive transcranial electrical stimulation is a category of neuromodulation techniques used for various disorders. Although medically approved devices exist, the variety of consumer electrical stimulation devices is increasing. Because clinical trials and animal tests are costly and risky, using a brain phantom can [...] Read more.
Non-invasive transcranial electrical stimulation is a category of neuromodulation techniques used for various disorders. Although medically approved devices exist, the variety of consumer electrical stimulation devices is increasing. Because clinical trials and animal tests are costly and risky, using a brain phantom can provide preliminary experimental validation. However, existing brain phantoms are often costly or require excessive preparation time, precluding their use for rapid, real-time optimization of stimulation settings. A limitation of direct electric fields in a phantom is the lack of 3D spatial resolution. Using well-researched modalities such as transcranial direct current stimulation (tDCS) and newer modalities such as amplitude-modulated transcranial pulsed-current stimulation (am-tPCS), a range of materials was tested for use as electrical phantoms. Based on cost, preparation time, and efficiency, ground beef and agar gel with a 10% salt mix were selected. The measured values for the total dosages were 0.55 W-s for am-tPCS and 0.91 W-s for tDCS. Due to a low gain on the recording electrodes, the signal efficiency measured against the power delivered was 4.2% for tDCS and 3.1% for am-tPCS. Issues included electrodes shifting in the soft material and the low sensitivity of the recording electrodes. Despite these issues, the effective combination of the phantom and recording methodologies can enable low costs and the rapid testing, experimentation, and verification of consumer neuromodulation devices in three dimensions. Additionally, the efficiency factors (EFs) between the observed dosage and the delivered dosage could streamline the comparison of experimental configurations. As demonstrated by comparing two types of electrical neuromodulation devices across the 3D space of a phantom, EFs can be used in conjunction with a cost-effective, time-expedient phantom to rapidly iterate and optimize stimulation parameters. Full article
(This article belongs to the Section Applied Neuroscience and Neural Engineering)
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<p>Thevenin equivalent circuit for electrical stimulation devices. Voltage V is in series with resistor R1 and resistive load R2.</p>
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<p>Individual circuit design of a measurement electrode probe array circuit design. Output recorded on Teensy microcontroller.</p>
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<p>Measurement volumes in phantom container in Overhead View (<b>A</b>) and Lateral View (<b>B</b>).</p>
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<p>Schematic of stimulation and data acquisition system, including stimulation device, recording arrangement, and phantom.</p>
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<p>Decrease in price and time for phantom preparation time.</p>
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<p>Perpendicular placement of stimulation electrodes with am-tPCS device in ground beef.</p>
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<p>Parallel placement of stimulation electrodes in am-tPCS in ground beef.</p>
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<p>Agar gel and 10% salt mix after removal of recording system and stimulation electrodes.</p>
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<p>Average voltage gradient from experimental measurements.</p>
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21 pages, 450 KiB  
Review
Noninvasive Electromagnetic Neuromodulation of the Central and Peripheral Nervous System for Upper-Limb Motor Strength and Functionality in Individuals with Cervical Spinal Cord Injury: A Systematic Review and Meta-Analysis
by Loreto García-Alén, Aina Ros-Alsina, Laura Sistach-Bosch, Mark Wright and Hatice Kumru
Sensors 2024, 24(14), 4695; https://doi.org/10.3390/s24144695 - 19 Jul 2024
Viewed by 1674
Abstract
(1) Background: Restoring arm and hand function is one of the priorities of people with cervical spinal cord injury (cSCI). Noninvasive electromagnetic neuromodulation is a current approach that aims to improve upper-limb function in individuals with SCI. The aim of this study is [...] Read more.
(1) Background: Restoring arm and hand function is one of the priorities of people with cervical spinal cord injury (cSCI). Noninvasive electromagnetic neuromodulation is a current approach that aims to improve upper-limb function in individuals with SCI. The aim of this study is to review updated information on the different applications of noninvasive electromagnetic neuromodulation techniques that focus on restoring upper-limb functionality and motor function in people with cSCI. (2) Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were used to structure the search protocol. A systematic review of the literature was performed in three databases: the Cochrane Library, PubMed, and Physiotherapy Evidence Database (PEDro). (3) Results: Twenty-five studies were included: four were on transcranial magnetic stimulation (TMS), four on transcranial direct current stimulation (tDCS), two on transcutaneous spinal cord stimulation (tSCS), ten on functional electrical stimulation (FES), four on transcutaneous electrical nerve stimulation (TENS), and one on neuromuscular stimulation (NMS). The meta-analysis could not be completed due to a lack of common motor or functional evaluations. Finally, we realized a narrative review of the results, which reported that noninvasive electromagnetic neuromodulation combined with rehabilitation at the cerebral or spinal cord level significantly improved upper-limb functionality and motor function in cSCI subjects. Results were significant compared with the control group when tSCS, FES, TENS, and NMS was applied. (4) Conclusions: To perform a meta-analysis and contribute to more evidence, randomized controlled trials with standardized outcome measures for the upper extremities in cSCI are needed, even though significant improvement was reported in each non-invasive electromagnetic neuromodulation study. Full article
(This article belongs to the Special Issue Feature Review Papers in Biosensors Section 2024)
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<p>PRISMA flow diagram of the systematic review process.</p>
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14 pages, 3053 KiB  
Article
Comparison of Transcranial Magnetic Stimulation Dosimetry between Structured and Unstructured Grids Using Different Solvers
by Francesca Camera, Caterina Merla and Valerio De Santis
Bioengineering 2024, 11(7), 712; https://doi.org/10.3390/bioengineering11070712 - 13 Jul 2024
Cited by 1 | Viewed by 1030
Abstract
In recent years, the interest in transcranial magnetic stimulation (TMS) has surged, necessitating deeper understanding, development, and use of low-frequency (LF) numerical dosimetry for TMS studies. While various ad hoc dosimetric models exist, commercial software tools like SimNIBS v4.0 and Sim4Life v7.2.4 are [...] Read more.
In recent years, the interest in transcranial magnetic stimulation (TMS) has surged, necessitating deeper understanding, development, and use of low-frequency (LF) numerical dosimetry for TMS studies. While various ad hoc dosimetric models exist, commercial software tools like SimNIBS v4.0 and Sim4Life v7.2.4 are preferred for their user-friendliness and versatility. SimNIBS utilizes unstructured tetrahedral mesh models, while Sim4Life employs voxel-based models on a structured grid, both evaluating induced electric fields using the finite element method (FEM) with different numerical solvers. Past studies primarily focused on uniform exposures and voxelized models, lacking realism. Our study compares these LF solvers across simplified and realistic anatomical models to assess their accuracy in evaluating induced electric fields. We examined three scenarios: a single-shell sphere, a sphere with an orthogonal slab, and a MRI-derived head model. The comparison revealed small discrepancies in induced electric fields, mainly in regions of low field intensity. Overall, the differences were contained (below 2% for spherical models and below 12% for the head model), showcasing the potential of computational tools in advancing exposure assessment required for TMS protocols in different bio-medical applications. Full article
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<p>Considered exposure scenarios for the numerical comparison: (<b>A</b>) single-shell sphere, (<b>B</b>) single-shell sphere with orthogonal slab, and (<b>C</b>) MRI-derived human head model.</p>
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<p>E-field induced in the sphere model on a section perpendicular to the coil and passing through the center of the sphere, calculated by (<b>A</b>) Sim4Life and (<b>B</b>) SimNIBS, and the <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>M</mi> <mi>A</mi> <mi>P</mi> <msub> <mi>E</mi> <mi>loc</mi> </msub> </mrow> </semantics></math> between the two software results (<b>C</b>). Input current: 1 A. Computing time: 6 s (SimNIBS) and 9 s (Sim4Life).</p>
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<p>E-field induced in the sphere with orthogonal slab model on a section parallel to the coil, perpendicular to the slab and passing through the center of the sphere, calculated by (<b>A</b>) Sim4Life and (<b>B</b>) SimNIBS and the <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>M</mi> <mi>A</mi> <mi>P</mi> <msub> <mi>E</mi> <mi>loc</mi> </msub> </mrow> </semantics></math> between the two software results (<b>C</b>). Input current: 1 A. Computing time: 6 s (SimNIBS) and 9 s (Sim4Life).</p>
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<p>E-field induced in the MRI-derived head model in the GM and WM on a transverse section passing through the <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>99.9</mn> <mi>th</mi> </mrow> </msub> </semantics></math>, calculated by (<b>A</b>) Sim4Life and (<b>B</b>) SimNIBS and the <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>M</mi> <mi>A</mi> <mi>P</mi> <msub> <mi>E</mi> <mi>loc</mi> </msub> </mrow> </semantics></math> between the two software results (<b>C</b>). Input current: 1 A. Computing time: 205 s (SimNIBS) and 122 s (Sim4Life).</p>
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<p>Stimulating Volume X (<math display="inline"><semantics> <mrow> <mi>S</mi> <msub> <mi>V</mi> <mi>X</mi> </msub> </mrow> </semantics></math>), i.e., the volume exposed to E-field equal to or greater than <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>%</mo> </mrow> </semantics></math> of <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>99.9</mn> <mi>th</mi> </mrow> </msub> </semantics></math> in MRI-derived human head model varying <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>%</mo> </mrow> </semantics></math>.</p>
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<p>Errors in the Stimulating Volume X (<math display="inline"><semantics> <mrow> <mi>S</mi> <msub> <mi>V</mi> <mi>X</mi> </msub> </mrow> </semantics></math>), i.e., the volume exposed to E-field equal to or greater than <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>%</mo> </mrow> </semantics></math> of <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>99.9</mn> <mi>th</mi> </mrow> </msub> </semantics></math>. (<b>A</b>) Single-shell sphere, (<b>B</b>) single-shell sphere with orthogonal slab, and (<b>C</b>) MRI-derived human head model.</p>
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<p>Comparison between the E-field obtained with analytical calculation (<a href="#FD6-bioengineering-11-00712" class="html-disp-formula">A1</a>) and the two software packages, calculated on <span class="html-italic">y</span>-axis (perpendicular to B-field and passing through the center of the sphere). Blue line: E-field obtained with analytic solution (<a href="#FD6-bioengineering-11-00712" class="html-disp-formula">A1</a>); red line: E-field calculated with Sim4Life; yellow line: E-field derived by multiplying SimNIBS results by a multiplicative factor k, which was determined through fitting the results to the analytical values (k = 0.02).</p>
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